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Understanding patterns of multimorbidity in the US older adult population and their relationship with mortality is important for reducing healthcare utilization and improving health. Previous investigations measured multimorbidity as counts of conditions rather than specific combination of conditions. This cross-sectional study with longitudinal mortality follow-up employed latent class analysis (LCA) to develop clinically meaningful subgroups of participants aged 50 and older with different combinations of 13 chronic conditions from the National Health Interview Survey 2002-2014. Mortality linkage with National Death Index was performed through December 2015 for 166,126 participants. Survival analyses were conducted to assess the relationships between LCA classes and all-cause mortality and cause specific mortalities. LCA identified five multimorbidity groups with primary characteristics: \"healthy\" (51.5%), \"age-associated chronic conditions\" (33.6%), \"respiratory conditions\" (7.3%), \"cognitively impaired\" (4.3%) and \"complex cardiometabolic\" (3.2%). Covariate-adjusted survival analysis indicated \"complex cardiometabolic\" class had the highest mortality with a Hazard Ratio (HR) of 5.30, 99.5% CI [4.52, 6.22]; followed by \"cognitively impaired\" class (3.34 [2.93, 3.81]); \"respiratory condition\" class (2.14 [1.87, 2.46]); and \"age-associated chronic conditions\" class (1.81 [1.66, 1.98]). Patterns of multimorbidity classes were strongly associated with the primary underlying cause of death. The \"cognitively impaired\" class reported similar number of conditions compared to the \"respiratory condition\" class but had significantly higher mortality (3.8 vs 3.7 conditions, HR = 1.56 [1.32, 1.85]). We demonstrated that LCA method is effective in classifying clinically meaningful multimorbidity subgroup. Specific combinations of conditions including cognitive impairment and depressive symptoms have a substantial detrimental impact on the mortality of older adults. The numbers of chronic conditions experienced by older adults is not always proportional to mortality risk. Our findings provide valuable information for identifying high risk older adults with multimorbidity to facilitate early intervention to treat chronic conditions and reduce mortality.

ABSTRACTObjectiveTo assess the influence of mild behavioral impairment (MBI) on the cognitive performance of older adults who are cognitively healthy or have mild cognitive impairment (MCI). MethodsSecondary data analysis of a sample ( n = 497) of older adults from the Florida Alzheimer’s Disease Research Center who were either cognitively healthy ( n = 285) or diagnosed with MCI ( n = 212). Over half of the sample ( n = 255) met the operationalized diagnostic criteria for MBI. Cognitive domains of executive function, attention, short-term memory, and episodic memory were assessed using a battery of neuropsychological tests. ResultsOlder adults with MBI performed worse on tasks of executive function, attention, and episodic memory compared to those without MBI. A significant interaction revealed that persons with MBI and MCI performed worse on tasks of episodic memory compared to individuals with only MCI, but no significant differences were found in performance in cognitively healthy older adults with or without MBI on this cognitive domain. As expected, cognitively healthy older adults performed better than individuals with MCI on every domain of cognition. ConclusionsThe present study found evidence that independent of cognitive status, individuals with MBI performed worse on tests of executive function, attention, and episodic memory than individuals without MBI. Additionally, those with MCI and MBI perform significantly worse on episodic memory tasks than individuals with only MCI. These results provide support for a unique cognitive phenotype associated with MBI and highlight the necessity for assessing both cognitive and behavioral symptoms.

To compare clinical, imaging, and neuropsychological characteristics and longitudinal course of subjects with pre-mild cognitive impairment (pre-MCI), who exhibit features of MCI on clinical examination but lack impairment on neuropsychological examination, to subjects with no cognitive impairment (NCI), nonamnestic MCI (naMCI), amnestic MCI (aMCI), and mild dementia. For 369 subjects, clinical dementia rating sum of boxes (CDR-SB), ApoE genotyping, cardiovascular risk factors, parkinsonism (UPDRS) scores, structural brain MRIs, and neuropsychological testing were obtained at baseline, whereas 275 of these subjects received an annual follow-up for 2–3 years. At baseline, pre-MCI subjects showed impairment on tests of executive function and language, higher apathy scores, and lower left hippocampal volumes (HPCV) in comparison to NCI subjects. Pre-MCI subjects showed less impairment on at least one memory measure, CDR-SB and UPDRS scores, in comparison to naMCI, aMCI and mild dementia subjects. Follow-up over 2–3 years showed 28.6% of pre-MCI subjects, but less than 5% of NCI subjects progressed to MCI or dementia. Progression rates to dementia were equivalent between naMCI (22.2%) and aMCI (34.5%) groups, but greater than for the pre-MCI group (2.4%). Progression to dementia was best predicted by the CDR-SB, a list learning and executive function test. This study demonstrates that clinically defined pre-MCI has cognitive, functional, motor, behavioral and imaging features that are intermediate between NCI and MCI states at baseline. Pre-MCI subjects showed accelerated rates of progression to MCI as compared to NCI subjects, but slower rates of progression to dementia than MCI subjects.

Recent advancements in plasma biomarkers, particularly p-tau217, have highlighted its potential for early Alzheimer's Disease (AD) detection. p-tau217 demonstrates diagnostic accuracy comparable to amyloid PET and cerebrospinal fluid (CSF) tests, with strong sensitivity and specificity to Aβ PET and tau PET results. This biomarker correlates with both current AD brain pathology and future cognitive decline, making it valuable for tracking disease progression. Although p-tau217 shows promise as a predictor for early AD detection, further research is needed to validate its utility in diverse populations before it can be established as a routine diagnostic tool. We conducted comprehensive clinical and neuropsychological evaluations of 96 Spanish-speaking Hispanic/Latino (H/L) and 56 Black/African American (B/AA) older adults. Participants underwent MRI, amyloid PET scans, and plasma biomarker testing, including p-tau217. A stepwise binary logistic regression analysis was performed to assess how well a cognitive challenge test could differentiate between cognitively unimpaired (CU) p-tau217- and amnestic mild cognitive impairment (aMCI) p-tau217+ B/AA individuals. Receiver operating characteristic (ROC) curve analyses using amyloid PET as the gold standard revealed a strong discriminative ability with an area under the curve of 0.89 (p < .001) for p-tau217 levels measured by SIMOA (Alzpath). Applying Youden's index, we identified a p-tau217 cut-off of 0.55 pg/ml that provided an optimal balance of sensitivity (85%) and specificity (87%). This cut-off was consistent across both H/L and B/AA groups. Cognitive challenge tests assessing proactive semantic interference (PSI) and intrusion errors were the best predictors of CU p-tau217- versus aMCI p-tau217+, with high sensitivity (80%) and specificity (91.7%). A p-tau217 cut-off of 0.55 pg/ml aligns well with positive Aβ PET scans, indicating underlying AD pathology, and provides an effective balance of sensitivity and specificity in a multicultural cohort. Additionally, cognitive challenge tests may enhance diagnostic accuracy, offering scalable potential for early AD detection in underserved populations.

Background Recent advancements in plasma biomarkers, particularly p‐tau217, have highlighted its potential for early Alzheimer's Disease (AD) detection. p‐tau217 demonstrates diagnostic accuracy comparable to amyloid PET and cerebrospinal fluid (CSF) tests, with strong sensitivity and specificity to Aβ PET and tau PET results. This biomarker correlates with both current AD brain pathology and future cognitive decline, making it valuable for tracking disease progression. Although p‐tau217 shows promise as a predictor for early AD detection, further research is needed to validate its utility in diverse populations before it can be established as a routine diagnostic tool. Method We conducted comprehensive clinical and neuropsychological evaluations of 96 Spanish‐speaking Hispanic/Latino (H/L) and 56 Black/African American (B/AA) older adults. Participants underwent MRI, amyloid PET scans, and plasma biomarker testing, including p‐tau217. A stepwise binary logistic regression analysis was performed to assess how well a cognitive challenge test could differentiate between cognitively unimpaired (CU) p‐tau217‐ and amnestic mild cognitive impairment (aMCI) p‐tau217+ B/AA individuals. Results Receiver operating characteristic (ROC) curve analyses using amyloid PET as the gold standard revealed a strong discriminative ability with an area under the curve of 0.89 (p < .001) for p‐tau217 levels measured by SIMOA (Alzpath). Applying Youden's index, we identified a p‐tau217 cut‐off of 0.55 pg/ml that provided an optimal balance of sensitivity (85%) and specificity (87%). This cut‐off was consistent across both H/L and B/AA groups. Cognitive challenge tests assessing proactive semantic interference (PSI) and intrusion errors were the best predictors of CU p‐tau217‐ versus aMCI p‐tau217+, with high sensitivity (80%) and specificity (91.7%). Conclusions A p‐tau217 cut‐off of 0.55 pg/ml aligns well with positive Aβ PET scans, indicating underlying AD pathology, and provides an effective balance of sensitivity and specificity in a multicultural cohort. Additionally, cognitive challenge tests may enhance diagnostic accuracy, offering scalable potential for early AD detection in underserved populations.

Using advances in machine learning for the diagnosis of Alzheimer's disease (AD) has attracted a lot of interest in recent years. However, most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this paper proposes a deep neural network based on modality fusion, kernelization, and tensorization to perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. More specifically, the proposed method explores the relationship between classification and longitudinal regression tasks to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject’s label and predict related cognitive scores at future timepoints from baseline. The proposed framework has been evaluated on a longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, involving 1117 subjects (328 CN, 191 MCI-C, 441 MCI-NC, and 157 AD). The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85±3.77. The prediction results show an average RMSE of 2.32±0.52 and a correlation of 0.71±5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification, and vice versa if the processing order is reversed. In other words, there is a breakpoint at which enhancing further the results of one process could lead to the downgrading of the accuracy for the other.

This study investigated the role of proactive semantic interference (frPSI) in predicting the progression of amnestic Mild Cognitive Impairment (aMCI) to dementia, taking into account various cognitive and biological factors. The research involved 89 older adults with aMCI who underwent baseline assessments, including amyloid PET and MRI scans, and were followed longitudinally over a period ranging from 12 to 55 months (average 26.05 months). The findings revealed that more than 30% of the participants diagnosed with aMCI progressed to dementia during the observation period. Using Cox Proportional Hazards modeling and adjusting for demographic factors, global cognitive function, hippocampal volume, and amyloid positivity, two distinct aspects of frPSI were identified as significant predictors of a faster decline to dementia. These aspects were fewer correct responses on a frPSI trial and a higher number of semantic intrusion errors on the same trial, with 29.5% and 31.6 % increases in the likelihood of more rapid progression to dementia, respectively. These findings after adjustment for demographic and biological markers of Alzheimer's Disease, suggest that assessing frPSI may offer valuable insights into the risk of dementia progression in individuals with aMCI.

Objective: Detection of cognitive impairment suggestive of risk for Alzheimer’s disease (AD) progression is crucial to the prevention of incipient dementia. This study was performed to determine if performance on a novel object discrimination task improved identification of earlier deficits in older adults at risk for AD. Method: In total, 135 participants from the 1Florida Alzheimer’s Disease Research Center [cognitively normal (CN), Pre-mild cognitive impairment (PreMCI), amnestic mild cognitive impairment (aMCI), and dementia] completed a test of object discrimination and traditional memory measures in the context of a larger neuropsychological and clinical evaluation. Results: The Object Recognition and Discrimination Task (ORDT) revealed significant differences between the PreMCI, aMCI, and dementia groups versus CN individuals. Moreover, relative risk of being classified as PreMCI rather than CN increased as an inverse function of ORDT score. Discussion: Overall, the obtained results suggest that a novel object discrimination task improves the detection of very early AD-related cognitive impairment, increasing the window for therapeutic intervention. (JINS, 2019, 25, 688–698)

INTRODUCTION Commercially available plasma p‐tau217 biomarker tests are not well studied in ethnically diverse samples. METHODS We evaluated associations between ALZPath plasma p‐tau217 and amyloid‐beta positron emission tomography (Aβ‐PET) in Hispanic/Latino (88% of Cuban or South American ancestry) and non‐Hispanic/Latino older adults. One‐ and two‐cutoff ranges were derived and evaluated to assess agreement with Aβ‐PET. RESULTS A total of 239 participants underwent blood draw and Aβ‐PET (age 70.8 ± 7.8, 55.2% female, education 15.6 ± 3.4 years, 48.9% Hispanic/Latino, 94.9% white). Plasma p‐tau217 showed excellent discrimination of Aβ‐PET positive and negative participants (visual read: AUC = 0.91 [0.87–0.95], p < 0.001; Centiloids quantification: AUC = 0.90 [0.86–0.94]). There was a greater percent agreement between low p‐tau217 and negative Aβ‐PET (95.8%) than high p‐tau217 and positive Aβ‐PET (86.3%). Analyses within ethnicity‐specific subgroups suggested similar p‐tau217 performance. DISCUSSION Plasma p‐tau217 (ALZPath) relates to brain Aβ in Hispanic/Latino and non‐Hispanic/Latino older adults. Independent validation and replication are necessary to establish reference ranges and inform appropriate contexts of use across ethno‐racially diverse populations. HIGHLIGHTS Plasma p‐tau217 (ALZPath) and Aβ‐PET were measured in Hispanic/Latino and non‐Hispanic/Latino older adults. Plasma p‐tau217 accurately discriminated Aβ‐PET positive and negative participants. Applying a two‐cutoff “intermediate” plasma p‐tau217 approach could reduce need for more invasive and costly testing. Plasma p‐tau217 associations with Aβ‐PET were strong within both Hispanic/Latino and non‐Hispanic/Latino groups.

Emerging evidence suggests that systemic physiological factors may influence plasma biomarker concentrations of Alzheimer’s disease (AD) and related neurodegenerative processes, potentially affecting their specificity for central nervous system pathology. This study examined the relationship of demographic factors and medical comorbidities with plasma biomarkers of AD and neurodegeneration in a community-dwelling cohort of Black/African American (B/AA) older adults (N = 141). Participants underwent plasma assessment of phosphorylated tau at threonine 217 (p-Tau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Results showed associations between plasma p-Tau217 and amyloid PET positivity, and significant intercorrelations among p-Tau217, GFAP, and NfL. Stepwise regression models incorporated demographics, amyloid PET status, and laboratory measures of renal, metabolic, and lipid function as predictors for each biomarker. p-Tau217 was primarily predicted by amyloid PET and renal function; GFAP by age and sex; and NfL by renal function, age, and sex. Findings indicate plasma biomarker concentrations in B/AA older adults reflect both central AD-related pathology and systemic physiological factors, particularly renal function, and demographic influences. Results underscore the importance of accounting for comorbid medical conditions and demographic characteristics when interpreting blood-based biomarkers and highlight the need for comprehensive medical phenotyping to improve diagnostic specificity and clinical utility.